Depression and anxiety are among the leading causes of disability and mortality in the US. Although effective treatments exist, response to specific treatments varies widely among individuals. More accurate matching of individuals with treatments could significantly increase overall success rates. Matching or personalization could be accomplished either by pre-treatment prediction of response or by earlier detection of treatment failure (shorting the cycle time of trial-and-errbr). Recent evidence suggests that measures of neuropsychological performance (such as the NIMH RDoC constructs) or directly observed behaviors could predict or detect response to treatment more accurately than traditional symptom measures. Increasing use of mobile electronic devices will make it possible to widely and rapidly disseminate tools for these next-generation assessments. The purpose of this pilot project is to assess consumer engagement, predictive utility, and clinical applicability of mobile, IT-enabled assessment of cognitive, physical and social activity in patients seeking treatment for depression and anxiety. The first aim will be to determine whether members of three health systems initiating treatment for depression and/or anxiety will complete cognitive assessment via internet and mobile devices, and whether members would be willing to collect and share physical and social activity data through mobile sensing devices or behavioral analytic apps installed on their smart phones. The second aim will be to explore the utility of the data collected prior to and during treatment in either prediction of treatment outcomes or early detection of treatment failure. The final aim will be to determine provider interest in and experience with accessing this information for treatment planning purposes.